Woo Youn Kim

Materials Chemistry · 2008–2020

A researcher in the Science corpus: 4 papers; trusts 8 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 47 citations into it; #10,053 of 11,513 by trust here, #6,473 by citations
• Artificial Intelligence — 14 citations into it
• Atomic and Molecular Physics, and Optics — 14 citations into it; #6,338 of 7,135 by trust here, #3,724 by citations
• Generative Models — 9 citations into it
• Electrical and Electronic Engineering — 7 citations into it; #14,759 of 16,539 by trust here, #8,322 by citations

Whom Woo Youn Kim trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Abraham Nitzan
2. Mark A. Ratner
3. Diederik P. Kingma
4. K. Novoselov

Who trusts Woo Youn Kim overall

1. O. Yazyev

Papers from Woo Youn Kim's perspective

Ordered by how strongly the researchers Woo Youn Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
3. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. ImageNet classification with deep convolutional neural networks
8. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
9. Attention is All you Need (Neural Information Processing Systems)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Prediction of very large values of magnetoresistance in a graphene nanoribbon device. (2008) — 691 citations
• Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation (2019) — 366 citations
• Molecular generative model based on conditional variational autoencoder for de novo molecular design (2018) — 408 citations
• PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions (2020) — 149 citations